






import math

import flax.linen as nn
import jax.numpy as jnp






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase = 1				,							_UpperCAmelCase = 1				,							_UpperCAmelCase = 1.0e4				,							_UpperCAmelCase = False				,							_UpperCAmelCase = 1.0				,							):
			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
			SCREAMING_SNAKE_CASE							=			float(embedding_dim // 2)
			SCREAMING_SNAKE_CASE							=			math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)
			SCREAMING_SNAKE_CASE							=			min_timescale * jnp.exp(jnp.arange(_UpperCAmelCase				,							dtype=jnp.floataa) * -log_timescale_increment)
			SCREAMING_SNAKE_CASE							=			jnp.expand_dims(_UpperCAmelCase				,							1) * jnp.expand_dims(_UpperCAmelCase				,							0)

			# scale embeddings
			SCREAMING_SNAKE_CASE							=			scale * emb

			if flip_sin_to_cos:
						SCREAMING_SNAKE_CASE							=			jnp.concatenate([jnp.cos(_UpperCAmelCase), jnp.sin(_UpperCAmelCase)]				,							axis=1)
			else:
						SCREAMING_SNAKE_CASE							=			jnp.concatenate([jnp.sin(_UpperCAmelCase), jnp.cos(_UpperCAmelCase)]				,							axis=1)
			SCREAMING_SNAKE_CASE							=			jnp.reshape(_UpperCAmelCase				,							[jnp.shape(_UpperCAmelCase)[0], embedding_dim])
			return signal

class 		_snake_case	(	nn.Module       ):
			_lowercase		:     int        =				32
			_lowercase		:     jnp.dtype        =				jnp.floataa






			@nn.compact
			def __call__(							self		,       a)   ->							int:
						SCREAMING_SNAKE_CASE							=			nn.Dense(self.time_embed_dim		,       dtype=self.dtype		,       name='linear_1')(a)
						SCREAMING_SNAKE_CASE							=			nn.silu(a)
						SCREAMING_SNAKE_CASE							=			nn.Dense(self.time_embed_dim		,       dtype=self.dtype		,       name='linear_2')(a)
						return temb



class 		_snake_case	(	nn.Module       ):
			_lowercase		:     int        =				32
			_lowercase		:     bool        =				False
			_lowercase		:     float        =				1






			@nn.compact
			def __call__(							self		,       a)   ->							str:
						return get_sinusoidal_embeddings(
						    a		,       embedding_dim=self.dim		,       flip_sin_to_cos=self.flip_sin_to_cos		,       freq_shift=self.freq_shift)












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




